{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1400, 6)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv(\"Selenium/machinelearning.csv\",encoding=\"utf-8\")\n",
    "df2 = pd.read_csv(\"Selenium/datascience.csv\",encoding=\"utf-8\")\n",
    "df3 = pd.read_csv(\"Selenium/datascience2.csv\",encoding=\"utf-8\")\n",
    "df4 = pd.read_csv(\"Selenium/data_analyst.csv\",encoding=\"utf-8\")\n",
    "df5 = pd.read_csv(\"Selenium/ai.csv\",encoding=\"utf-8\")\n",
    "\n",
    "df = pd.concat([df1,df2,df3,df4,df5], axis=0).drop_duplicates()\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Organic      1217\n",
       "Sponsored     183\n",
       "Name: Sponsored, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Sponsored\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1217, 6)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df.Sponsored != 'Sponsored']\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11f257a20>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Task 1: Find companies with highest number of job listings\n",
    "counts = df.groupby(\"Company\").count()[\"Title\"].sort_values(ascending=False)[:20]\n",
    "counts.plot(\"bar\",figsize=(20,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location\n",
       "Bengaluru, Karnataka    374\n",
       "Pune, Maharashtra       201\n",
       "Mumbai, Maharashtra      93\n",
       "Hyderabad, Telangana     93\n",
       "Gurgaon, Haryana         60\n",
       "Delhi, Delhi             43\n",
       "India                    41\n",
       "Chennai, Tamil Nadu      39\n",
       "Noida, Uttar Pradesh     36\n",
       "Ahmedabad, Gujarat       21\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Task 2: Find Locations with highest number of job listings\n",
    "loc_counts = df.groupby(\"Location\").count()[\"Title\"].sort_values(ascending=False)[:10]\n",
    "loc_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "253597.5625"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def format_salary(row):\n",
    "    salary = row[\"Salary\"]\n",
    "    if \"-\" in salary:\n",
    "        split = salary.split(\"-\")\n",
    "        salary_min = split[0]\n",
    "        salary_max = split[1]\n",
    "    else:\n",
    "        salary_min = salary\n",
    "        salary_max = salary\n",
    "    \n",
    "    row[\"salary_min\"] = salary_min.replace(\"₹\",\"\").replace(\"a month\",\"\").replace(\"a year\",\"\").replace(\",\",\"\")\n",
    "    row[\"salary_max\"] = salary_max.replace(\"₹\",\"\").replace(\"a month\",\"\").replace(\"a year\",\"\").replace(\",\",\"\")\n",
    "       \n",
    "    if \"month\" in row[\"Salary\"]:\n",
    "        row[\"salary_min\"] = int(row[\"salary_min\"])*12\n",
    "        row[\"salary_max\"] = int(row[\"salary_max\"])*12\n",
    "    \n",
    "   \n",
    "    return row\n",
    "\n",
    "\n",
    "df_salary = df[df[\"Salary\"]!= \"None\"].dropna()\n",
    "df_salary = df_salary.apply(format_salary,axis=1) \n",
    "\n",
    "df_salary[\"salary_min\"] = pd.to_numeric(df_salary[\"salary_min\"],'coerce')\n",
    "df_salary[\"salary_max\"] = pd.to_numeric(df_salary[\"salary_max\"],'coerce')\n",
    "\n",
    "df_salary[\"salary_min\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cleanData(desc):\n",
    "    desc = word_tokenize(desc)\n",
    "    desc = [word.lower() for word in desc if word.isalpha() and len(word) > 2]\n",
    "    desc = [word for word in desc if word not in stop_words]\n",
    "    return desc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "from nltk import word_tokenize\n",
    "\n",
    "from nltk.corpus import stopwords\n",
    "stop_words = stopwords.words('english')\n",
    "\n",
    "tags_df = df[\"Description\"].apply(cleanData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "result = tags_df.apply(Counter).sum().items()\n",
    "result = sorted(result, key=lambda kv: kv[1],reverse=True)\n",
    "result_series = pd.Series({k: v for k, v in result})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "#skills = [\"tableau\",\"power\",\"qlik\"]\n",
    "#skills = [\"aws\",\"azure\"]\n",
    "#skills = [\"nltk\",\"pandas\",\"numpy\",\"matplotlib\",\"jupyter\",\"opencv\"]\n",
    "#skills = [\"statistics\",\"machine\",\"deep\",\"neural\",\"predictive\"]\n",
    "skills = [\"experienced\",\"expert\",\"intern\",\"intermediate\",\"begineer\",\"fresher\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13a79c898>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "filter_series = result_series.filter(items=skills)\n",
    "filter_series.plot('bar',figsize=(20,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
